Antisocial online behavior detection using deep learning
Digitalization shifts human communication to online platforms, which has many benefits but also builds up a space for antisocial online behavior (AOB) such as harassment, insult and other forms of hateful textual content. Online platforms have good reasons to monitor and moderate such content. The p...
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Published in | Decision Support Systems Vol. 138; p. 113362 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
01.11.2020
Elsevier Sequoia S.A |
Subjects | |
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Abstract | Digitalization shifts human communication to online platforms, which has many benefits but also builds up a space for antisocial online behavior (AOB) such as harassment, insult and other forms of hateful textual content. Online platforms have good reasons to monitor and moderate such content. The paper examines the viability of automatic content monitoring using deep machine learning and natural language processing (NLP). More specifically, we consolidate prior work in the field of antisocial online behavior detection and compare relevant approaches to recent NLP models in an empirical study. Covering important methodological advancements in NLP including bidirectional encoding, attention, hierarchical text representations, and pre-trained transformer-based language models, and extending previous approaches by introducing a pseudo-sentence hierarchical attention network, the paper provides a comprehensive summary of the state-of-affairs in NLP-based AOB detection, clarifies the detection accuracy that is attainable with today's technology, discusses whether this degree is sufficient for deploying deep learning-based text screening systems, and approaches the interpretability topic.
•Comprehensive benchmark on deep learning regimes for AOB detection.•Usage of transformer-based language models.•Interpretability module to understand the model's logic.•Interpretability module to detect unintended bias. |
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AbstractList | Digitalization shifts human communication to online platforms, which has many benefits but also builds up a space for antisocial online behavior (AOB) such as harassment, insult and other forms of hateful textual content. Online platforms have good reasons to monitor and moderate such content. The paper examines the viability of automatic content monitoring using deep machine learning and natural language processing (NLP). More specifically, we consolidate prior work in the field of antisocial online behavior detection and compare relevant approaches to recent NLP models in an empirical study. Covering important methodological advancements in NLP including bidirectional encoding, attention, hierarchical text representations, and pre-trained transformer-based language models, and extending previous approaches by introducing a pseudo-sentence hierarchical attention network, the paper provides a comprehensive summary of the state-of-affairs in NLP-based AOB detection, clarifies the detection accuracy that is attainable with today's technology, discusses whether this degree is sufficient for deploying deep learning-based text screening systems, and approaches the interpretability topic.
•Comprehensive benchmark on deep learning regimes for AOB detection.•Usage of transformer-based language models.•Interpretability module to understand the model's logic.•Interpretability module to detect unintended bias. Digitalization shifts human communication to online platforms, which has many benefits but also builds up a space for antisocial online behavior (AOB) such as harassment, insult and other forms of hateful textual content. Online platforms have good reasons to monitor and moderate such content. The paper examines the viability of automatic content monitoring using deep machine learning and natural language processing (NLP). More specifically, we consolidate prior work in the field of antisocial online behavior detection and compare relevant approaches to recent NLP models in an empirical study. Covering important methodological advancements in NLP including bidirectional encoding, attention, hierarchical text representations, and pre-trained transformer-based language models, and extending previous approaches by introducing a pseudo-sentence hierarchical attention network, the paper provides a comprehensive summary of the state-of-affairs in NLP-based AOB detection, clarifies the detection accuracy that is attainable with today's technology, discusses whether this degree is sufficient for deploying deep learning-based text screening systems, and approaches the interpretability topic. |
ArticleNumber | 113362 |
Author | Zinovyeva, Elizaveta Lessmann, Stefan Härdle, Wolfgang Karl |
Author_xml | – sequence: 1 givenname: Elizaveta surname: Zinovyeva fullname: Zinovyeva, Elizaveta email: elizaveta.zinovyeva@hu-berlin.de organization: School of Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany – sequence: 2 givenname: Wolfgang Karl surname: Härdle fullname: Härdle, Wolfgang Karl email: haerdle@hu-berlin.de organization: School of Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany – sequence: 3 givenname: Stefan surname: Lessmann fullname: Lessmann, Stefan email: stefan.lessmann@hu-berlin.de organization: School of Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany |
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Keywords | Deep learning Attention mechanism Natural language processing Antisocial online behavior Cyberbullying Text classification |
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SubjectTerms | Antisocial online behavior Attention mechanism Cyberbullying Deep learning Digitization Human communication Machine learning Natural language processing Text classification |
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